Setup

# Gather parameters from command line  
#dir.create(file.path(resultsPath,"cache"), showWarnings=F, recursive=T)
nCores <- parallel::detectCores()#params$nCores
subsetGenes <- params$subsetGenes 
subsetCells <- params$subsetCells 
resolution <-  as.numeric(params$resolution)   

root <- getwd()
# Have to setwd via knitr
# knitr::opts_knit$set(root.dir=resultsPath, child.path = resultsPath)
knitr::opts_chunk$set(echo=T, error=T, root.dir = resultsPath#cache=T, cache.lazy=T,     
                      ) 
 
kableStyle = c("striped", "hover", "condensed", "responsive") 
# Utilize parallel processing later on 
print(paste("**** Utilized Cores **** =", nCores))

## [1] "**** Utilized Cores **** = 4"

params

## $subsetGenes
## [1] "protein_coding"
## 
## $subsetCells
## [1] "400"
## 
## $resolution
## [1] "0.6"
## 
## $resultsPath
## [1] "/Users/schilder/Desktop/PD_scRNAseq/Results/subsetGenes-protein_coding__subsetCells-400__Resolution-0.6"

** /Users/schilder/Desktop/PD_scRNAseq/Results/subsetGenes-protein_coding__subsetCells-400__Resolution-0.6 **

Load Libraries & Report Versions

library(Seurat)
library(dplyr)
library(gridExtra)
library(knitr)
library(kableExtra) 
library(plotly)
library(ggplot2)
library(reshape2)
library(shiny) 
# 
# install.packages('devtools')
# devtools::install_github('talgalili/heatmaply')
## Install Bioconductor
#  if (!requireNamespace("BiocManager"))
#     install.packages("BiocManager")
# BiocManager::install(c("biomaRt"))
library(biomaRt)  
## Seurat::FindGeneTerms() # Enrichr API
## Seurat::MultiModal_CCA() # Integrates data from disparate datasets (CIA version too)

sessionInfo()

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14.1
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] biomaRt_2.38.0   shiny_1.2.0      reshape2_1.4.3   plotly_4.8.0    
##  [5] kableExtra_0.9.0 knitr_1.21       gridExtra_2.3    dplyr_0.7.8     
##  [9] Seurat_2.3.4     Matrix_1.2-15    cowplot_0.9.3    ggplot2_3.1.0   
## 
## loaded via a namespace (and not attached):
##   [1] snow_0.4-3           backports_1.1.3      Hmisc_4.1-1         
##   [4] plyr_1.8.4           igraph_1.2.2         lazyeval_0.2.1      
##   [7] splines_3.5.1        digest_0.6.18        foreach_1.4.4       
##  [10] htmltools_0.3.6      lars_1.2             gdata_2.18.0        
##  [13] magrittr_1.5         checkmate_1.8.5      memoise_1.1.0       
##  [16] cluster_2.0.7-1      mixtools_1.1.0       ROCR_1.0-7          
##  [19] readr_1.3.1          R.utils_2.7.0        prettyunits_1.0.2   
##  [22] colorspace_1.3-2     blob_1.1.1           rvest_0.3.2         
##  [25] xfun_0.4             crayon_1.3.4         RCurl_1.95-4.11     
##  [28] jsonlite_1.6         bindr_0.1.1          survival_2.43-3     
##  [31] zoo_1.8-4            iterators_1.0.10     ape_5.2             
##  [34] glue_1.3.0           gtable_0.2.0         kernlab_0.9-27      
##  [37] prabclus_2.2-6       BiocGenerics_0.28.0  DEoptimR_1.0-8      
##  [40] scales_1.0.0         mvtnorm_1.0-8        DBI_1.0.0           
##  [43] bibtex_0.4.2         Rcpp_1.0.0           metap_1.0           
##  [46] dtw_1.20-1           progress_1.2.0       viridisLite_0.3.0   
##  [49] xtable_1.8-3         htmlTable_1.12       reticulate_1.10     
##  [52] foreign_0.8-71       bit_1.1-14           proxy_0.4-22        
##  [55] mclust_5.4.2         SDMTools_1.1-221     Formula_1.2-3       
##  [58] stats4_3.5.1         tsne_0.1-3           htmlwidgets_1.3     
##  [61] httr_1.4.0           gplots_3.0.1         RColorBrewer_1.1-2  
##  [64] fpc_2.1-11.1         acepack_1.4.1        modeltools_0.2-22   
##  [67] ica_1.0-2            pkgconfig_2.0.2      XML_3.98-1.16       
##  [70] R.methodsS3_1.7.1    flexmix_2.3-14       nnet_7.3-12         
##  [73] tidyselect_0.2.5     rlang_0.3.0.1        later_0.7.5         
##  [76] AnnotationDbi_1.44.0 munsell_0.5.0        tools_3.5.1         
##  [79] RSQLite_2.1.1        ggridges_0.5.1       evaluate_0.12       
##  [82] stringr_1.3.1        yaml_2.2.0           npsurv_0.4-0        
##  [85] bit64_0.9-7          fitdistrplus_1.0-11  robustbase_0.93-3   
##  [88] caTools_1.17.1.1     purrr_0.2.5          RANN_2.6            
##  [91] bindrcpp_0.2.2       pbapply_1.3-4        nlme_3.1-137        
##  [94] mime_0.6             R.oo_1.22.0          xml2_1.2.0          
##  [97] hdf5r_1.0.1          compiler_3.5.1       rstudioapi_0.8      
## [100] png_0.1-7            lsei_1.2-0           tibble_1.4.2        
## [103] stringi_1.2.4        lattice_0.20-38      trimcluster_0.1-2.1 
## [106] pillar_1.3.1         Rdpack_0.10-1        lmtest_0.9-36       
## [109] data.table_1.11.8    bitops_1.0-6         irlba_2.3.2         
## [112] gbRd_0.4-11          httpuv_1.4.5.1       R6_2.3.0            
## [115] latticeExtra_0.6-28  promises_1.0.1       KernSmooth_2.23-15  
## [118] IRanges_2.16.0       codetools_0.2-16     MASS_7.3-51.1       
## [121] gtools_3.8.1         assertthat_0.2.0     withr_2.1.2         
## [124] S4Vectors_0.20.1     diptest_0.75-7       parallel_3.5.1      
## [127] doSNOW_1.0.16        hms_0.4.2            grid_3.5.1          
## [130] rpart_4.1-13         tidyr_0.8.2          class_7.3-14        
## [133] rmarkdown_1.11       segmented_0.5-3.0    Rtsne_0.15          
## [136] Biobase_2.42.0       base64enc_0.1-3

print(paste("Seurat ", packageVersion("Seurat")))

## [1] "Seurat  2.3.4"

Load Data

#setwd("~/Desktop/PD_scRNAseq/")
dir.create(file.path(root,"Data"), showWarnings=F) 
load(file.path(root,"Data/seurat_object_add_HTO_ids.Rdata"))
pbmc <- seurat.obj  
rm(seurat.obj)

Pre-filtered Dimensions

pbmc

## An object of class seurat in project RAJ_13357 
##  24914 genes across 22113 samples.

Clean Metadata

Add Metadata

metadata <- read.table(file.path(root,"Data/meta.data4.tsv"))
kable(head(metadata), caption = "Metadata") %>%  
  kable_styling(bootstrap_options = kableStyle) %>%
  scroll_box(width = "100%", height = "500px")

Metadata
	ID	nGene	nUMI	orig.ident	singlet.or.not.binary	percent.mito	res.2	res.1	res.0.6	res.0.3	barcode	dx	mut	Ethnicity	Gender	Age
AAAGCAAGTTTGTTGG	BIMD0007	784	1780	RAJ_13357	1	0.0314607	3	3	1	0	AAAGCAAGTTTGTTGG	PD	PD	White	M	59
TCAGCAATCTTGACGA	BIMD0007	742	1854	RAJ_13357	1	0.0302213	18	1	0	0	TCAGCAATCTTGACGA	PD	PD	White	M	59
AGCTCCTTCGCGTAGC	BIMD0007	495	988	RAJ_13357	1	0.0404858	14	7	2	3	AGCTCCTTCGCGTAGC	PD	PD	White	M	59
TATTACCCACTCTGTC	BIMD0007	812	1874	RAJ_13357	1	0.0469584	16	12	7	6	TATTACCCACTCTGTC	PD	PD	White	M	59
CTCGAGGAGCGATTCT	BIMD0007	863	2260	RAJ_13357	1	0.0212578	1	0	1	0	CTCGAGGAGCGATTCT	PD	PD	White	M	59
ATAAGAGCATCAGTCA	BIMD0007	803	2034	RAJ_13357	1	0.0216323	9	8	0	0	ATAAGAGCATCAGTCA	PD	PD	White	M	59

# Make AgeGroups
makeAgeGroups <- function(){
  dim(metadata)
  getMaxRound <- function(vals=metadata$Age, unit=10)unit*ceiling((max(vals)/unit))
  getMinRound <- function(vals=metadata$Age, unit=10)unit*floor((min(vals)/unit)) 
   
  ageBreaks = c(seq(getMinRound(), getMaxRound(), by = 10), getMaxRound()+10)
  AgeGroupsUniq <- c()
  for (i in 1:(length(ageBreaks)-1)){ 
    AgeGroupsUniq <- append(AgeGroupsUniq, paste(ageBreaks[i],ageBreaks[i+1], sep="-")) 
  } 
  data.table::setDT(metadata,keep.rownames = T,check.names = F)[, AgeGroups := cut(Age, 
                                  breaks = ageBreaks, 
                                  right = F, 
                                  labels = AgeGroupsUniq,
                                  nclude.lowest=T)]
  metadata <- data.frame(metadata)
  unique(metadata$AgeGroups)
  head(metadata)
  dim(metadata)
  return(metadata)
}
# metadata <- makeAgeGroups()

pbmc <- AddMetaData(object = pbmc, metadata = metadata)  
# Get rid of any NAs (cells that don't match up with the metadata) 
if(subsetCells==F){
  pbmc <- FilterCells(object = pbmc,  subset.names = "nGene", low.thresholds = 0)
} else {pbmc <- FilterCells(object = pbmc,  subset.names = "nGene", low.thresholds = 0,
                    # Subset for testing 
                    cells.use = pbmc@cell.names[0:subsetCells]
                    )
}

Filter & Normalize Data

Subset Genes by Biotype

Include only subsets of genes by type. Biotypes from: https://useast.ensembl.org/info/genome/genebuild/biotypes.html

subsetBiotypes <- function(pbmc, subsetGenes){
  if( subsetGenes!=F ){
    print(paste("Subsetting genes:",subsetGenes))
    # If the gene_biotypes file exists, import csv. Otherwise, get from biomaRt
    if(file_test("-f", file.path(root,"Data/gene_biotypes.csv"))){
      biotypes <- read.csv(file.path(root,"Data/gene_biotypes.csv"))
    }
    else {
      ensembl <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="grch37.ensembl.org",
                       dataset="hsapiens_gene_ensembl") 
      ensembl <- useDataset(mart = ensembl, dataset = "hsapiens_gene_ensembl")
      listFilters(ensembl)
      listAttributes(ensembl)   
      biotypes <- getBM(attributes=c("hgnc_symbol", "gene_biotype"), filters="hgnc_symbol",
            values=row.names(pbmc@data), mart=ensembl) 
      write.csv(biotypes, file.path(root,"Data/gene_biotypes.csv"), quote=F, row.names=F)
    } 
    # Subset data by creating new Seurat object (annoying but necessary)
    geneSubset <- biotypes[biotypes$gene_biotype==subsetGenes,"hgnc_symbol"] 
    
    print(paste(dim(pbmc@raw.data[geneSubset, ])[1],"/", dim(pbmc@raw.data)[1], 
                "genes are", subsetGenes))
    # Add back into pbmc 
    subset.matrix <- pbmc@raw.data[geneSubset, ] # Pull the raw expression matrix from the original Seurat object containing only the genes of interest
    pbmc_sub <- CreateSeuratObject(subset.matrix) # Create a new Seurat object with just the genes of interest
    orig.ident <- row.names(pbmc@meta.data) # Pull the identities from the original Seurat object as a data.frame
    pbmc_sub <- AddMetaData(object = pbmc_sub, metadata = pbmc@meta.data) # Add the idents to the meta.data slot
    pbmc_sub <- SetAllIdent(object = pbmc_sub, id = "ident") # Assign identities for the new Seurat object
    pbmc <- pbmc_sub
    rm(list = c("pbmc_sub","geneSubset", "subset.matrix", "orig.ident")) 
  } 
}

subsetBiotypes(pbmc, subsetGenes)

## [1] "Subsetting genes: protein_coding"
## [1] "14827 / 24914 genes are protein_coding"

Subset Cells

Filter by cells, normalize , filter by gene variability.

pbmc <- FilterCells(object = pbmc, subset.names = c("nGene", "percent.mito"), 
    low.thresholds = c(200, -Inf), high.thresholds = c(2500, 0.05))

pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize", 
    scale.factor = 10000)

Subset Genes by Variance

** Important!**: Specify do.par = T, and num.cores = nCores in ‘ScaleData’ to use all available cores.

# Store the top most variable genes in @var.genes
pbmc <- FindVariableGenes(object = pbmc, mean.function = ExpMean, dispersion.function = LogVMR,
    x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)

# IMPORTANT!: Must set do.par=T and num.cors = n for large datasets being processed on computing clusters
pbmc <- ScaleData(object = pbmc, vars.to.regress = c("nUMI", "percent.mito"), do.par = T, num.cores = nCores)

## Regressing out: nUMI, percent.mito

## 
## Time Elapsed:  7.3254029750824 secs

## Scaling data matrix

Filtered Dimensions

pbmc

## An object of class seurat in project RAJ_13357 
##  24914 genes across 396 samples.

Diagnostic Plots

Violin Plots

vp <- VlnPlot(object = pbmc, features.plot = c("nGene", "nUMI", "percent.mito"),nCol = 3, do.return = T) %>% + ggplot2::aes(alpha=0.5)
vp

Gene Plots

percent.mito plot

# par(mfrow = c(1, 2))
gp1 <- GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "percent.mito", pch.use=20, 
         do.hover=T, data.hover = "mut")

gp1

nGene plot

gp2 <- GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "nGene", pch.use=20, 
         do.hover=T, data.hover = "mut")

gp2

Dimensionality Reduction

PCA

ProjectPCA scores each gene in the dataset (including genes not included in the PCA) based on their correlation with the calculated components. Though we don’t use this further here, it can be used to identify markers that are strongly correlated with cellular heterogeneity, but may not have passed through variable gene selection. The results of the projected PCA can be explored by setting use.full=T in the functions above

Other Dim Reduction Methods in Seurat
RunCCA()
RunMultiCCA()
RunDiffusion()
RunPHATE()
RunUMAP()
RunICA()

# Run PCA with only the top most variables genes
pbmc <- RunPCA(object = pbmc, pc.genes = pbmc@var.genes, do.print=F) #, pcs.print = 1:5,  genes.print = 5

VizPCA

VizPCA(object = pbmc, pcs.use = 1:2)

PCA plot

PCAPlot(object = pbmc, dim.1 = 1, dim.2 = 2, do.hover=T, data.hover="mut")

PCHeatmaps

pbmc <- ProjectPCA(object = pbmc, do.print=F) 
# 'PCHeatmap' is a wrapper for heatmap.2 
# PCA Heatmap: PC1-PCn
PCHeatmap(object = pbmc, pc.use = 1:12, do.balanced=T, label.columns=F, use.full=F)

# 
# PCHeatmap_interactive <- function(PC=1){  
#   PC_dat <- PCHeatmap(object = pbmc, pc.use = PC, do.return = T) 
#   # Cluster samples
#   Xclust <- pcp %>% dist(upper = T) %>% hclust() 
#   Yclust <- PC_dat %>% t() %>% dist(upper = T) %>% hclust() 
#   PC_dat <- PC_dat[Xclust$order, Yclust$order]  
#   # Plotly 
#   plot_ly(y=row.names(PC_dat), z=matrix(PC_dat), type = "heatmap", 
#           colors =viridis::plasma(n=100))
# }
# PCHeatmap_interactive(PC=1)

Significant PCs

Determine statistically significant PCs for further analysis. NOTE: This process can take a long time for big datasets, comment out for expediency. More approximate techniques such as those implemented in PCElbowPlot() can be used to reduce computation time

#pbmc <- JackStraw(object = pbmc, num.replicate = 100, display.progress = FALSE)
PCElbowPlot(object = pbmc)

Find Cell Clusters

We first construct a KNN graph based on the euclidean distance in PCA space, and refine the edge weights between any two cells based on the shared overlap in their local neighborhoods (Jaccard similarity). To cluster the cells, we apply modularity optimization techniques such as the Louvain algorithm (default) or SLM [SLM, Blondel et al., Journal of Statistical Mechanics], to iteratively group cells together, with the goal of optimizing the standard modularity function.

On Resolution
The FindClusters function implements the procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells. Optimal resolution often increases for larger datasets. The clusters are saved in the object@ident slot.

# TRY DIFFERENT RESOLUTIONS
pbmc <- FindClusters(object = pbmc, reduction.type = "pca", dims.use = 1:10, resolution = resolution, print.output = 0, save.SNN = T) 
PrintFindClustersParams(object = pbmc)

## Parameters used in latest FindClusters calculation run on: 2019-01-05 03:41:20
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function    Algorithm         n.start         n.iter
##      1                   1                 100             10
## -----------------------------------------------------------------------------
## Reduction used          k.param          prune.SNN
##      pca                 30                0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10

t-SNE

As input to the tSNE, we suggest using the same PCs as input to the clustering analysis, although computing the tSNE based on scaled gene expression is also supported using the genes.use argument.

** Important!**: Specify num_threads=0 in ‘RunTSNE’ to use all available cores.

labSize <- 6
#pbmc <- StashIdent(object = pbmc, save.name = "pre_clustering") 
#pbmc <- SetAllIdent(object = pbmc, id = "pre_clustering") 

pbmc <- RunTSNE(object=pbmc,  reduction.use = "pca", dims.use = 1:10, do.fast = TRUE, 
                tsne.method = "Rtsne", num_threads=0) # num_threads
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = pbmc, do.label=T, label.size = labSize, do.return=T) %>% ggplotly()

t-SNE + Metadata Plots

tSNE_metadata_plot <- function(var){
  print(paste("t-SNE Metadata plot for ", var))
  # Metadata plot 
  p1 <- TSNEPlot(pbmc, do.return = T,  do.label = T,  group.by = var, pt.size=1,
                 plot.title=paste("Color by ",var), vector.friendly=T) %>% ggplotly() %>% 
     layout(legend = list(orientation = 'h', xanchor = "center", x = 0.5, y = .999)) 
  # t-SNE clusters plot
  p2 <- TSNEPlot(pbmc, do.return = T, do.label = T, pt.size=1,
                 plot.title=paste("Color by Clusters"), vector.friendly=T) %>% ggplotly() %>% 
  layout(legend = list(orientation = 'h', xanchor = "center", x = 0.5, y = .999)) 
  #print(plot_grid(ggplotly(p1), ggplotly(p2)))
  fluidPage( 
    fluidRow(
      column(6, p1), column(6, p2) 
    )
  )
}   
# metaVars <- c(dx","mut","Gender","Age")
# 
# for (var in metaVars){
#   print(paste("t-SNE Metadata plot for ",var))
#   # Metadata plot 
#   p1 <- TSNEPlot(pbmc, do.return = T, pt.size = 0.5, group.by = var, do.label = T, 
#                  dark.theme=F, plot.title=paste("Color by ",var))
#   # t-SNE clusters plot
#   p2 <- TSNEPlot(pbmc, do.label = T, do.return = T, pt.size = 0.5, plot.title=paste("Color by t-SNE clusters"))
#   print(plot_grid(p1, p2))
# }

Disease

tSNE_metadata_plot("dx")

## [1] "t-SNE Metadata plot for  dx"

Mutations

tSNE_metadata_plot("mut")

## [1] "t-SNE Metadata plot for  mut"

Gender

tSNE_metadata_plot("Gender")

## [1] "t-SNE Metadata plot for  Gender"

Age

tSNE_metadata_plot("Age")

## [1] "t-SNE Metadata plot for  Age"

Cluster Biomarkers

Seurat has several tests for differential expression which can be set with the test.use parameter (see the DE vignette for details). For example, the ROC test returns the ‘classification power’ for any individual marker (ranging from 0 - random, to 1 - perfect).

### Biomarkers: All Clusters vs. All Other Clusters ***
# find markers for every cluster compared to all remaining cells, report
# only the positive ones
pbmc.markers <- FindAllMarkers(object = pbmc, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
topBiomarkers <- pbmc.markers %>% group_by(cluster) %>% top_n(2, avg_logFC)
 
kable(topBiomarkers) %>% kable_styling(bootstrap_options = kableStyle)

avg_logFC	pct.1	pct.2	p_val_adj	cluster	gene
0.8504918	0.861	0.560	0e+00	0	RNU1-60P
0.8592522	0.679	0.280	1e-07	0	RNVU1-14
1.9717156	0.730	0.068	0e+00	1	FCER1A
1.2098444	0.900	0.530	0e+00	1	HLA-DQA1

Cluster Biomarker Tests

getTopBiomarker <- function(pbmc.markers, clusterID, topN=1){
  df <- subset(pbmc.markers, p_val_adj<0.05 & cluster==as.character(clusterID)) %>% arrange(desc(avg_logFC))
  top_pct_markers <- df[1:topN,"gene"]
  return(top_pct_markers)
}
# clust1_biomarkers <- getTopBiomarker(pbmc.markers, clusterID=1, topN=2)
# clust2_biomarkers <- getTopBiomarker(pbmc.markers, clusterID=2, topN=2)


### Plot biomarkers 
plotBiomarkers <- function(pbmc, biomarkers, cluster){
  biomarkerPlots <- list()
  for (marker in biomarkers){ 
    p <- VlnPlot(object = pbmc, features.plot = c(marker), y.log=T, return.plotlist=T) 
    biomarkerPlots[[marker]] <- p + ggplot2::aes(alpha=0.5) + xlab( "Cluster") + ylab( "Expression")
  }
  combinedPlot <- do.call(grid.arrange, c(biomarkerPlots, list(ncol=2, top=paste("Top DEG Biomarkers for Cluster",cluster))) ) 

  # biomarkerPlots <- lapply(biomarkers, function(marker) {
  #   VlnPlot(object = pbmc, features.plot = c(marker), y.log=T, return.plotlist=T) %>% + ggplot2::ggtitle(marker) %>% ggplotly() 
  # })    
  # return(subplot(biomarkerPlots) )
}   

top1 <- pbmc.markers %>% group_by(cluster) %>% top_n(1, avg_logFC) 
nCols <- floor( sqrt(length(unique(top1$cluster))) )   
figHeight <- nCols *7

# Plot top 2 biomarker genes for each 
for (clust in unique(pbmc.markers$cluster)){ 
   cat('\n')   
   cat("### Cluster ",clust,"\n") 
   biomarkers <- getTopBiomarker(pbmc.markers, clusterID=clust, topN=2)
   plotBiomarkers(pbmc, biomarkers, clust)  
   cat('\n')   
}

Cluster 0

Cluster 1

Top Biomarker Plot

Biomarkers tSNE

fp <- FeaturePlot(object = pbmc, features.plot = top1$gene, cols.use = c("grey", "purple"), 
    reduction.use = "tsne", nCol = nCols, do.return = T)

Biomarkers Heatmap

top5 <- pbmc.markers %>% group_by(cluster) %>% top_n(5, avg_logFC)
# setting slim.col.label to TRUE will print just the cluster IDS instead of
# every cell name
DoHeatmap(object = pbmc, genes.use = top5$gene, slim.col.label=T, remove.key=T) %>% ggplotly()

Biomarkers Ridgeplot

RidgePlot(pbmc, features.plot = top1$gene,  nCol = nCols, do.sort = F)

## Picking joint bandwidth of 0.256

## Picking joint bandwidth of 0.208

Map Clusters to Marker-Defined Cell Types

Known Monocytes Biomarkers
Classical: CD14++ / CD16–
Intermediate: CD14++ / CD16+
Nonclassical: CD14+ / CD16++ (not captured in this data)

markerList <- c("CD14", "FCGR3A") 

genes_by_cluster <- function(pbmc, markerList){
  marker.matrix <- pbmc@scale.data[row.names(pbmc@scale.data) %in% markerList, ]   
  markerMelt <- reshape2:::melt.matrix(marker.matrix)
  colnames(markerMelt) <- c("Gene", "Cell", "Expression")
  identData <- data.frame(pbmc@ident)  
  identData$Cell <- row.names(identData)
  colnames(identData) <- c("Cluster","Cell") 
  markerDF <- merge(markerMelt,  identData, by = "Cell")
 
  return(markerDF)
}
markerDF <- genes_by_cluster(pbmc, markerList)

Known Biomarkers: Heatmaps

Clusters Averaged

# Show mean exp for each marker
avgMarker <- markerDF %>% group_by(Gene, Cluster) %>% summarise(meanExp = mean(Expression)) 
ggplot(data = avgMarker, aes(x=Gene, y=Cluster, fill=meanExp)) %>% + geom_tile() %>% + scale_fill_distiller(palette="viridis") %>% ggplotly()

## Warning in pal_name(palette, type): Unknown palette viridis

Clusters Separated

markerMelt <- reshape2::acast(markerDF, Cell~Gene, value.var="Expression", fun.aggregate = mean, drop = F, fill = 0) 

#plot_ly(  z = markerMelt, y=row.names(markerMelt), z=colnames(markerMelt), type="heatmap")
# dx_colors <- colorRampPalette(brewer.pal(2, "RdBu"))
# mut_colors <- colorRampPalette(brewer.pal(length(unique(pbmc@meta.data$mut)), "Set3"))
Spectral <- grDevices::colorRampPalette(RColorBrewer::brewer.pal(length(unique(pbmc@meta.data$mut)), "Spectral"))
# Spectral <- heatmaply::Spectral(length(unique(pbmc@meta.data$mut)))

heatmaply::heatmaply(markerMelt,  key.title="Expression",#plot_method= "plotly",
          k_row = length(unique(pbmc.markers)), dendrogram = "row",
          showticklabels = c(T, F), xlab = "Known Markers", ylab = "Cells", column_text_angle = 0,
          row_side_colors =  pbmc@meta.data[,c("dx","mut")], row_side_palette = Spectral
          )  %>%  colorbar(tickfont = list(size = 12), titlefont = list(size = 14), which = 2)  %>% 
          colorbar(tickfont = list(size = 12), titlefont = list(size = 14), which = 1)

## Warning in doTryCatch(return(expr), name, parentenv, handler): unable to load shared object '/Library/Frameworks/R.framework/Resources/modules//R_X11.so':
##   dlopen(/Library/Frameworks/R.framework/Resources/modules//R_X11.so, 6): Library not loaded: /opt/X11/lib/libSM.6.dylib
##   Referenced from: /Library/Frameworks/R.framework/Resources/modules//R_X11.so
##   Reason: image not found

## Warning: Didn't find a colorbar to modify.

## Warning: Didn't find a colorbar to modify.

Known Biomarkers: Boxplot

ggplot(data = markerDF, aes(x=Cluster, y=Expression, fill=Gene)) %>% 
  + geom_boxplot(alpha=0.5) %>% + scale_fill_manual(values=c("purple", "turquoise")) # %>% ggplotly()

Known Biomarkers: tSNE

expressionTSNE <- function(pbmc, marker, colors=c("grey", "red")){
  FeaturePlot(object = pbmc, features.plot = marker, cols.use = colors, 
    reduction.use = "tsne", nCol=2, do.return = T, dark.theme = T)[[1]] %>% ggplotly()
}
 
 subplot(expressionTSNE(pbmc, markerList[1]), 
        expressionTSNE(pbmc, markerList[2],  colors=c("grey", "green")))

Label Clusters by Known Biomarker

current.cluster.ids <- unique(pbmc.markers$cluster) #c(0, 1, 2, 3, 4, 5, 6, 7)
top1 <- pbmc.markers %>% group_by(cluster) %>% top_n(1, avg_logFC)
new.cluster.ids <- top1$gene #c("CD4 T cells", "CD14+ Monocytes", "B cells", "CD8 T cells", "FCGR3A+ Monocytes", "NK cells", "Dendritic cells", "Megakaryocytes")

pbmc@ident <- plyr::mapvalues(x = pbmc@ident, from = current.cluster.ids, to = new.cluster.ids)
TSNEPlot(object=pbmc, do.label=T, pt.size=0.5, do.return=T) %>% ggplotly()

Cell Sub-clusters

Further subdivisions within cell types.
If you perturb some of our parameter choices above (for example, setting resolution=0.8 or changing the number of PCs), you might see the CD4 T cells subdivide into two groups. You can explore this subdivision to find markers separating the two T cell subsets. However, before reclustering (which will overwrite object@ident), we can stash our renamed identities to be easily recovered later.

Assign Identity

# First lets stash our identities for later
pbmc <- StashIdent(object = pbmc, save.name = "ClusterNames_0.6")

# Note that if you set save.snn=T above, you don't need to recalculate the
# SNN, and can simply put: pbmc <- FindClusters(pbmc,resolution = 0.8)
pbmc <- FindClusters(object = pbmc, reduction.type = "pca", dims.use = 1:10, 
    resolution = 0.8, print.output = FALSE)

## Warning in BuildSNN(object = object, genes.use = genes.use, reduction.type
## = reduction.type, : Build parameters exactly match those of already
## computed and stored SNN. To force recalculation, set force.recalc to TRUE.

## Warning in BuildSNN(object = object, genes.use = genes.use, reduction.type
## = reduction.type, : Build parameters exactly match those of already
## computed and stored SNN. To force recalculation, set force.recalc to TRUE.

# Demonstration of how to plot two tSNE plots side by side, and how to color
# points based on different criteria
plot1 <- TSNEPlot(object = pbmc, do.return = TRUE, no.legend = TRUE, do.label = TRUE, label.size=labSize)
plot2 <- TSNEPlot(object = pbmc, do.return = TRUE, group.by = "ClusterNames_0.6", 
                  no.legend = TRUE, do.label = TRUE, label.size=labSize)
plot_grid(plot1, plot2)

Find Markers

# Find discriminating markers
tcell.markers <- FindMarkers(object = pbmc, ident.1 = 0, ident.2 = 1)

# Most of the markers tend to be expressed in C1 (i.e. S100A4). However, we
# can see that CCR7 is upregulated in C0, strongly indicating that we can
# differentiate memory from naive CD4 cells.  cols.use demarcates the color
# palette from low to high expression
FeaturePlot(object = pbmc, features.plot = top1$gene, cols.use = c("green", "blue"))

pbmc <- SetAllIdent(object = pbmc, id = "ClusterNames_0.6")

# Save results for EACH run (in their respective subfolders)
saveRDS(pbmc, file=file.path(params$resultsPath, "cd14-processed.rds") )

Parkinson’s Disease scRNA-seq: CD14++ Monocytes

Brian M. Schilder, Bioinformatician II Raj Lab Department of Neuroscience Icahn School of Medicine at Mount Sinai NYC, New York

12/26/2018